The long-term goal of our project is to develop tools that can be used to diagnose and assess sleep disorders unobtrusively in a patient's home. Sleep disorders and sleep deprivation are significant public health problems. The U.S. Institute of Medicine estimates that 70 million Americans suffer from chronic, treatable sleep disorders. One of the most common and problematic sleep disorders is obstructive sleep apnea (OSA), where a partial collapse or obstruction of the pharyngeal airway results in intermittent reduction in blood oxygen saturation and disruption of sleep. The traditional gold standard for diagnosing and monitoring these disorders is overnight polysomnography (PSG). Unfortunately PSG is an expensive, obtrusive, and inconvenient test in which multiple sensors are attached to patients who are already struggling with sleep. A simpler tool used to screen for sleep disordered breathing (SDB) in a patient's home over multiple nights would help clinicians decide if PSG is indicated, providing a much needed and currently unavailable window into a patient's apnea status over multiple nights in their natural sleep environment. In this study, we will continue ou efforts to understand how load cells could be used for this purpose. We have used load cells under the supports of the bed to quantify the frequency and severity of apneas and hypopneas. We now seek to understand how this system could be simplified and used effectively in a home setting. Our Specific Aims are: (1) to develop improved algorithms to detect lying position on the bed during load cell collection~ (2) develop algorithms to separate the breathing and movement signals from two individuals sharing a bed~ and (3) to determine the most effective minimal configuration of load cells (number, location, and loading) that can be used to accurately measure the severity of sleep apnea.